CUTS DISTRIBUTION COSTS 18% LOGISTICS PYTHON

Network Optimisation Model

Python linear programming model (PuLP + scipy) evaluating warehouse locations, lane allocations, and transport mode assignments to minimise total distribution cost while satisfying service-level constraints across a 12-node European network.

Project Overview

⚑ Problem

Suboptimal Legacy Network

The distribution network was designed 8 years ago for a different customer mix. Four warehouse locations were chosen based on real estate availability, not demand centroids. Demand has shifted significantly since, resulting in 23% of lanes operating well above optimal cost-per-km and 14% of SLA commitments at risk due to distance misalignment.

⚙ Solution

LP Network Optimisation (PuLP)

A Python model formulates the network as a minimum-cost flow problem: minimise Σ(cost per lane × volume) subject to supply balance at each warehouse, demand satisfaction at each customer node, capacity constraints, and maximum service lead-time per SLA band. Candidate warehouse locations evaluated via p-median heuristic.

✓ Findings

18% Cost Reduction — €1.4M Saved

The optimised network consolidates from 4 to 3 active DCs, shifts 8 road lanes to rail, and eliminates 6 suboptimal cross-docking points. Total annual distribution cost drops from €7.8M to €6.4M — an 18% saving. SLA compliance improves from 81% to 97%. Full implementation over 9 months.

Interactive Network Visualisation

European Distribution Network — Current vs Optimised

Warehouse / DC

Customer Node

Closed (Optimised)

Road

Rail

Sea

Eliminated Lane

CURRENT TOTAL COST

€7.82M

Annual distribution

OPTIMISED TOTAL COST

€6.41M

▼ 18.0% reduction

ANNUAL SAVING

€1.41M

Vs current network

SLA COMPLIANCE

97%

▲ from 81% current

DCs ACTIVE

3 / 4

1 DC consolidated

LANES SHIFTED TO RAIL

Road → Rail modal shift

Dataset Explorer

Network Cost Analysis

Current vs Optimised Cost per Corridor (€K)

Mode Split — Current vs Optimised (Volume)

Distance vs Cost/Unit — Bubble by Volume

SLA Compliance by Corridor

Savings Opportunity by Lane (€K)

Cost per Unit-KM by Mode

Lane Register

LANE ID	ORIGIN	DESTINATION	MODE	DIST (KM)	VOLUME (UNITS)	TRANSPORT (€)	HANDLING (€)	TOTAL COST (€)	OPT SAVING (€)	SLA DAYS	ACTUAL DAYS	SLA MET	STATUS

Cost Reduction Roadmap

Monthly Distribution Cost — Current to Optimised (12-Month Implementation)

Company Advice

💡 Recommendations for Logistics Strategy Teams

Network redesign typically delivers 15–25% cost reduction — but only if the model constraints are realistic. Include maximum service lead-time per customer tier and warehouse capacity ceilings as hard constraints, not soft guidelines.
Run the model annually at minimum: demand pattern shifts, fuel costs, and new customer acquisitions can erode an optimised network back to suboptimal within 18 months without re-validation.
Before closing any DC, model the transition cost (inventory rebalancing, lease break penalties, customer communication) against the steady-state saving. Payback under 18 months is the typical go/no-go threshold.
Rail modal shift only pencils out above 400km lane distance. Below this threshold, the handling cost premium and longer lead time erode the freight saving — validate your minimum distance before applying the model recommendation.
Build in a network resilience constraint: the LP will always consolidate to the theoretical minimum nodes, but single-node failure risk increases proportionally. Require at minimum dual-source coverage for all Tier-1 customer zones.
Involve commercial and customer service teams early — a network that cuts cost but degrades service on key accounts destroys more value than it saves. Run sensitivity analysis on SLA tightening before presenting to the CFO.

Key Takeaway: The LP model finds the mathematical optimum, but the implementable optimum is always a constrained version of it. The value of the model is not the final answer — it's the structured conversation it forces between logistics, finance, and commercial about what constraints actually matter.